def mk_data(n_samples=200, random_state=0, separability=1,
noise_corr=2, dim=100):
rng = np.random.RandomState(random_state)
y = rng.random_integers(0, 1, size=n_samples)
noise = rng.normal(size=(n_samples, dim))
if not noise_corr is None and noise_corr > 0:
noise = ndimage.gaussian_filter1d(noise, noise_corr, axis=0)
noise = noise / noise.std(axis=0)
# We need to decrease univariate separability as dimension increases
centers = 4. / dim * np.ones((2, dim))
centers[0] *= -1
X = separability * centers[y] + noise
return X, y
###############################################################################
# Code to run the cross-validations
cross_validation_simulations.py 文件源码
python
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